Dual-Process LiDAR Localization with Run-Time Mode Switching: Complementary Failure Signals and an Environment-Dependent Trade-Off

A mobile platform carrying a three-dimensional LiDAR typically uses one localization configuration for a whole mission, yet a horizontal slice of that laser matched against a two-dimensional grid is inexpensive where structure constrains the pose and unreliable in open space and among repeated façades. This paper presents a dual-process system that runs a three-dimensional LiDAR-inertial estimator and a two-dimensional particle filter in parallel and decides at runtime which drives the published pose. The estimates are not fused: one output is selected and the standby channel is throttled rather than stopped, so computation can be separated by mode within one run and compared against a time-aligned baseline. A policy combines four ground-truth-free switching metrics—scan-to-map agreement, particle dispersion, registration fitness and cross-estimator disagreement—through a dwell-time state machine. On three segments of a long-term public dataset, mapped from a different session, the low-compute mode uses 32% and 33% less processor time over the same ground, and in every segment the segment-level planar ATE differs from the three-dimensional baseline by at most 0.084 m. Over a whole traversal the outcome depends on the dwell fraction and on the always-on overhead of both paths: −18% at a dwell fraction of 72% for 0.045 m of added error, +4% at 24%, and +16% when the dwell fraction is zero. Particle dispersion and cross-estimator disagreement carry complementary information about the two ways planar localization fails. The measurements do not indicate that switching is universally beneficial; they show that its trade-off can be measured for a given environment.